TY - GEN
T1 - Lithology discrimination using seismic elastic attributes
T2 - 16th Genetic and Evolutionary Computation Conference, GECCO 2014
AU - Da Praxedes, Eric S.
AU - Koshiyama, Adriano S.
AU - Dias, Douglas M.
AU - Vellasco, Marley M.B.R.
AU - Pacheco, Marco A.C.
AU - Abreu, Elita S.
PY - 2014
Y1 - 2014
N2 - One of the most important issues in oil & gas industry is the lithological identification. Lithology is the macroscopic description of the physical characteristics of a rock. This work proposes a new methodology for lithological discrimination, using GPF-CLASS model (Genetic Programming for Fuzzy Classification) a Genetic Fuzzy System based on Multi-Gene Genetic Programming. The main advantage of our approach is the possibility to identify, through seismic patterns, the rock types in new regions without requiring opening wells. Thus, we seek for a reliable model that provides two exibilities for the experts: evaluate the membership degree of a seismic pattern to the several rock types and the chance to analyze at linguistic level the model output. Therefore, the final tool must afford knowledge discovery and support to the decision maker. Also, we evaluate other 7 classification models (from statistics and computational intelligence), using a database from a well located in Brazilian coast. The results demonstrate the potentialities of GPF-CLASS model when comparing to other classifiers.
AB - One of the most important issues in oil & gas industry is the lithological identification. Lithology is the macroscopic description of the physical characteristics of a rock. This work proposes a new methodology for lithological discrimination, using GPF-CLASS model (Genetic Programming for Fuzzy Classification) a Genetic Fuzzy System based on Multi-Gene Genetic Programming. The main advantage of our approach is the possibility to identify, through seismic patterns, the rock types in new regions without requiring opening wells. Thus, we seek for a reliable model that provides two exibilities for the experts: evaluate the membership degree of a seismic pattern to the several rock types and the chance to analyze at linguistic level the model output. Therefore, the final tool must afford knowledge discovery and support to the decision maker. Also, we evaluate other 7 classification models (from statistics and computational intelligence), using a database from a well located in Brazilian coast. The results demonstrate the potentialities of GPF-CLASS model when comparing to other classifiers.
KW - Fuzzy classification systems
KW - Genetic programming
KW - Oil & gas industry
UR - https://www.scopus.com/pages/publications/84905674678
U2 - 10.1145/2576768.2598319
DO - 10.1145/2576768.2598319
M3 - Conference contribution
AN - SCOPUS:84905674678
SN - 9781450326629
T3 - GECCO 2014 - Proceedings of the 2014 Genetic and Evolutionary Computation Conference
SP - 1151
EP - 1157
BT - GECCO 2014 - Proceedings of the 2014 Genetic and Evolutionary Computation Conference
PB - Association for Computing Machinery
Y2 - 12 July 2014 through 16 July 2014
ER -